Ordinal Logistic Regression Analysis in Effective Teaching Practices

Abstract

: The Association of College and University Educators (ACUE) course in effective teaching practices has been applied to an undergraduate mathematics class at Texas A and M International University (TAMIU) for the past two years (Spring 2022, Fall 2022 and Spring 2023). Practices such as ensuring equitable access to learning, embracing diversity in the classroom, providing useful feedback and teaching powerful note-taking skills were applied throughout the whole semester in class. This study focuses on the comparison between the same class in Spring 2022 when ACUE’s effective teaching practices were not present and the classes in which ACUE was present in Fall 2022 and Spring 2023; in total, 89 students from two classes were analyzed with identification data removed. Ordinal logistic regression is used to analyze how effective teaching practices are related to students' final grades by analyzing students' grades, assignment submissions and class participation in different classes. Fisher’s exact test is applied to data analysis to check how effective teaching practices affect class participation; results show that for students who were from the class with ACUE practices, the odds of being more likely (i.e., “very” with a value of 2 or “somewhat likely” with a value of 1 versus “unlikely” with a value of 0) to have a passing grade is 1.92 times that of students who were from the class without ACUE practices, holding constant all other variables such as homework assignments and class participation. The impact of effective teaching practices is statistically significant on class participation (p-value = 0.04); this study concurs with the previous studies that ACUE’s effective teaching practices have positively improved students’ performance and should be applied to college classrooms. Ordinal logistic regression was the first time applied in analyzing